Peptide and Protein Quantification Using Automated Immuno-MALDI (iMALDI)
Bibliographic record
Abstract
Mass spectrometry (MS) is one of the most commonly used technologies for quantifying proteins in complex samples, with excellent assay specificity as a result of the direct detection of the mass-to-charge ratio of each target molecule. However, MS-based proteomics, like most other analytical techniques, has a bias towards measuring high-abundance analytes, so it is challenging to achieve detection limits of low ng/mL or pg/mL in complex samples, and this is the concentration range for many disease-relevant proteins in biofluids such as human plasma. To assist in the detection of low-abundance analytes, immuno-enrichment has been integrated into the assay to concentrate and purify the analyte before MS measurement, significantly improving assay sensitivity. In this work, the immuno- Matrix-Assisted Laser Desorption/Ionization (iMALDI) technology is presented for the quantification of proteins and peptides in biofluids, based on immuno-enrichment on beads, followed by MALDI-MS measurement without prior elution. The anti-peptide antibodies are functionalized on magnetic beads, and incubated with samples. After washing, the beads are directly transferred onto a MALDI target plate, and the signals are measured by a MALDI-Time of Flight (MALDI-TOF) instrument after the matrix solution has been applied to the beads. The sample preparation procedure is simplified compared to other immuno-MS assays, and the MALDI measurement is fast. The whole sample preparation is automated with a liquid handling system, with improved assay reproducibility and higher throughput. In this article, the iMALDI assay is used for determining the peptide angiotensin I (Ang I) concentration in plasma, which is used clinically as readout of plasma renin activity for the screening of primary aldosteronism (PA).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".